The network is symmetrical, so the decoder network is similar to the encoder network with the convolutional layers replaced by transposed convolutional layers to upsample the spatial dimensions of the input.
The last transposed convolutional layer only has three filters so that the shape of the output is identical to the input and the loss function can be calculated from the feature map-channel pairs.
The activation function in the last layer of the network is a Sigmoid to constrain the output values between 0 and 1.
The optimizer Adamax is used, \(i.e.\)  a variant of Adaptive Moment estimation (Adam) [1]}.
This network architecture contains \(1,160,847\)  trainable parameters.
It took approximately 138.9 hours to train it for 1000 epochs to a training MSE of \(9.4 \times 10^{-5}\)  on our hardware.
